Use Case 4.2: Leverage the deeper graph to surface more relevant=
works

Use Case 4.2: Leverage the deeper graph to surface more relevant wo=
rks

Example Story: As a researcher, I would like to see resources in respons=
e to a search where the relevance ranking of the results reflects the "impo=
rtance" of the works, based on how they have been used or selected by other=
s, so that I can find important resources that might otherwise be "hidden" =
in a large set of results.

In this use case the importance calculated will reflect=
importance in the scholarly world and will be different from those in comm=
odity systems, as well as including items that would not appear in commodit=
y systems (e.g. manuscripts). A benchmark will be doing better than Amazon =
with richer results.

Out of scope: n/a

Potential Demonstrations

A. Do a "page-rank" style algorithm (centrality, minimu=
m path, etc) across the full linked data graph, assigning appropriate weigh=
ts to certain kinds of annotations and relationships and reflecting those w=
eights in the relevance ranking of search results for a set of common queri=
es.

B. Possibly allow (for demo purposes) the user to see t=
he comparative results s/he would have gotten from Amazon. (Added by DavidW=
, not discussed by group)

C. Boost the ranking of any resource that has external =
relationship links by a computation over those relationships.

Data Sources

Annotations on resources

Scrape references from LibGuides

Any relationships between resources or links to them in the broader lin=
ked-data web

Usage data of any sort (StackScore or similar would be a start)

Ontology Requirements

None new beyond ability to include annotations, references, relationshi=
ps and usage data as described data sources

Engineering=
Work

Understanding of works and granularity at which links between items sho=
uld be understood to understand importance

Deal with questions of how to work in a rather data sparse environment =
-- how to merge information from page-rank like ordering with some other or=
der that doesn=E2=80=99t rely on a dense graph

Will need to do iterative/experimental work on what will influence rank=
. Possibilities include:

e.g., a Libguide that cites a work -- ideally with an OCLC number or DO=
I

could scrape links into the catalog

usage data

external ranking

Might use various axes of similarity: Example -- Griffin Weber=E2=80=99=
s analysis that is performed nightly and stored in the triple store in a di=
fferent namespace -- geographic proximity, association with MeSH terms calc=
ulated from occurrence in publications